With the depth maps predicted by ConvNets, learning based SLAM systems can integrate depth information to address some limitations of classical monocular solution. For example, CNN-SLAM [1]} utilizes the learned depths from single images into a monocular SLAM framework (i.e. LSD-SLAM [2]}). Their experiment shows how the learned depth maps contribute to mitigate the absolute scale recovery problem in pose estimates and scene reconstruction. CNN-SLAM achieves dense scene predictions even in texture-less areas, which is normally hard for a conventional SLAM system.
